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Optimization is a key concept in mathematics, computer science, and operations research, and is essential to the modeling of any system, playing an integral role in computer-aided design. Fundamentals of Optimization Techniques with Algorithms presents a complete package of various traditional and advanced optimization techniques along with a variety of example problems, algorithms and MATLAB(c) code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice.
Dr Sukanta Nayak is Assistant Professor in the Department of Mathematics, at the Amrita School of Engineering in Coimbatore, India. He previously held a postdoctoral research fellowship at the University of Johannesburg, South Africa, and received his Ph.D. in mathematics from the National Institute of Technology Rourkela, in India. His research interests include numerical analysis, linear algebra, fuzzy finite element method, fuzzy heat, neutron diffusion equations, fuzzy stochastic differential equations and wavelet analysis. He has published widely in the field, including as co-author of a book entitled Interval Finite Element Method with MATLAB, for Elsevier's Academic Press (2018).
1. Introduction to optimization2. Linear programming3. Single-variable nonlinear optimization4. Multivariable unconstrained nonlinear optimization5. Multivariable constrained nonlinear optimization6. Geometric programming7. Dynamic programming8. Integer programming9. Multiobjective optimization10. Nature-inspired optimization
